{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\n'this file check the 2D pose data'\\n'-----------------'\\n0'Right Ankle', 3\\n1'Right Knee', 2\\n2'Right Hip', 1\\n3'Left Hip', 4\\n4'Left Knee', 5\\n5'Left Ankle', 6\\n6'Right Wrist', 15\\n7'Right Elbow', 14\\n8'Right Shoulder', 13\\n9'Left Shoulder', 10\\n10'Left Elbow', 11\\n11'Left Wrist', 12\\n12'Neck', 8 \\n13'Top of Head', 9\\n14'Pelvis)', 0\\n15'Thorax', 7 \\n16'Spine', mpi3d\\n17'Jaw', mpi3d\\n18'Head', mpi3d\\n\\nmpi3dval: reorder = [14,2,1,0,3,4,5,16,12,18,9,10,11,8,7,6]\\n\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "'this file check and convert the pose data'\n",
    "'-----------------'\n",
    "0'Right Ankle', 3\n",
    "1'Right Knee', 2\n",
    "2'Right Hip', 1\n",
    "3'Left Hip', 4\n",
    "4'Left Knee', 5\n",
    "5'Left Ankle', 6\n",
    "6'Right Wrist', 15\n",
    "7'Right Elbow', 14\n",
    "8'Right Shoulder', 13\n",
    "9'Left Shoulder', 10\n",
    "10'Left Elbow', 11\n",
    "11'Left Wrist', 12\n",
    "12'Neck', 8 \n",
    "13'Top of Head', 9\n",
    "14'Pelvis)', 0\n",
    "15'Thorax', 7 \n",
    "16'Spine', mpi3d\n",
    "17'Jaw', mpi3d\n",
    "18'Head', mpi3d\n",
    "\n",
    "mpi3dval: reorder = [14,2,1,0,3,4,5,16,12,18,9,10,11,8,7,6]\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def show2Dpose(channels, ax, lcolor=\"#3498db\", rcolor=\"#e74c3c\", add_labels=True):\n",
    "    \"\"\"\n",
    "    Visualize a 2d skeleton\n",
    "\n",
    "    Args\n",
    "    channels: 64x1 vector. The pose to plot.\n",
    "    ax: matplotlib axis to draw on\n",
    "    lcolor: color for left part of the body\n",
    "    rcolor: color for right part of the body\n",
    "    add_labels: whether to add coordinate labels\n",
    "    Returns\n",
    "    Nothing. Draws on ax.\n",
    "    \"\"\"\n",
    "    vals = np.reshape( channels, (-1, 2) )\n",
    "    #plt.plot(vals[:,0], vals[:,1], 'ro')\n",
    "    I  = np.array([0,1,2,0,4,5,0,7,8,8,10,11,8,13,14]) # start points\n",
    "    J  = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]) # end points\n",
    "    LR = np.array([1,1,1,0,0,0,0, 0, 0, 0, 0, 0, 1, 1, 1], dtype=bool)\n",
    "\n",
    "    # Make connection matrix\n",
    "    for i in np.arange( len(I) ):\n",
    "        x, y = [np.array( [vals[I[i], j], vals[J[i], j]] ) for j in range(2)]\n",
    "        ax.plot(x, -y, lw=2, c=lcolor if LR[i] else rcolor)\n",
    "\n",
    "    RADIUS = 1 # space around the subject\n",
    "    xroot, yroot = vals[0,0], vals[0,1]\n",
    "    \n",
    "    ax.set_xlim([-1, 1])\n",
    "    ax.set_ylim([-1, 1])\n",
    "    \n",
    "    if add_labels:\n",
    "        ax.set_xlabel(\"x\")\n",
    "        ax.set_ylabel(\"-y\")\n",
    "\n",
    "    ax.set_aspect('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "def show3Dpose(channels, ax, lcolor=\"#3498db\", rcolor=\"#e74c3c\", add_labels=True,\n",
    "               gt=False,pred=False): # blue, orange\n",
    "    \"\"\"\n",
    "    Visualize a 3d skeleton\n",
    "\n",
    "    Args\n",
    "    channels: 96x1 vector. The pose to plot.\n",
    "    ax: matplotlib 3d axis to draw on\n",
    "    lcolor: color for left part of the body\n",
    "    rcolor: color for right part of the body\n",
    "    add_labels: whether to add coordinate labels\n",
    "    Returns\n",
    "    Nothing. Draws on ax.\n",
    "    \"\"\"\n",
    "\n",
    "    #   assert channels.size == len(data_utils.H36M_NAMES)*3, \"channels should have 96 entries, it has %d instead\" % channels.size\n",
    "    vals = np.reshape( channels, (16, -1) )\n",
    "\n",
    "    I  = np.array([0,1,2,0,4,5,0,7,8,8,10,11,8,13,14]) # start points\n",
    "    J  = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]) # end points\n",
    "    LR  = np.array([1,1,1,0,0,0,0, 0, 0, 0, 0, 0, 0, 1, 1, 1], dtype=bool)\n",
    "\n",
    "    # Make connection matrix\n",
    "    for i in np.arange( len(I) ):\n",
    "        x, y, z = [np.array( [vals[I[i], j], vals[J[i], j]] ) for j in range(3)]\n",
    "        if gt:\n",
    "            ax.plot(x,z, -y,  lw=2, c='k')\n",
    "        elif pred:\n",
    "            ax.plot(x,z, -y,  lw=2, c='r')\n",
    "        else:\n",
    "            ax.plot(x, z, -y,  lw=2, c=lcolor if LR[i] else rcolor)\n",
    "\n",
    "    RADIUS = 1 # space around the subject\n",
    "    xroot, yroot, zroot = vals[0,0], vals[0,1], vals[0,2]\n",
    "    ax.set_xlim3d([-RADIUS+xroot, RADIUS+xroot])\n",
    "    ax.set_ylim3d([-RADIUS+zroot, RADIUS+zroot])\n",
    "    ax.set_zlim3d([-RADIUS-yroot, RADIUS-yroot])\n",
    "\n",
    "\n",
    "    if add_labels:\n",
    "        ax.set_xlabel(\"x\")\n",
    "        ax.set_ylabel(\"z\")\n",
    "        ax.set_zlabel(\"-y\")\n",
    "\n",
    "    # Get rid of the panes (actually, make them white)\n",
    "    white = (1.0, 1.0, 1.0, 0.0)\n",
    "    ax.w_xaxis.set_pane_color(white)\n",
    "    ax.w_yaxis.set_pane_color(white)\n",
    "    # Keep z pane\n",
    "\n",
    "    # Get rid of the lines in 3d\n",
    "    ax.w_xaxis.line.set_color(white)\n",
    "    ax.w_yaxis.line.set_color(white)\n",
    "    ax.w_zaxis.line.set_color(white)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'deal with mpi3dval'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''deal with mpi3dval'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['imgname', 'center', 'scale', 'part', 'S']\n"
     ]
    }
   ],
   "source": [
    "# load the download data\n",
    "\n",
    "mpi3dval_path = './dataset_extras/mpi_inf_3dhp_valid.npz'\n",
    "mpi_inf_3dhp_valid = np.load(mpi3dval_path)\n",
    "print(mpi_inf_3dhp_valid.files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert the data to a list to processing.\n",
    "\n",
    "mpi3d_val_list = []\n",
    "for i in range(2929):\n",
    "    tmp_dict = {}\n",
    "    tmp_dict['filename'] = mpi_inf_3dhp_valid['imgname'][i]\n",
    "    tmp_dict['kpts2d'] = mpi_inf_3dhp_valid['part'][i]    \n",
    "    tmp_dict['kpts3d'] = mpi_inf_3dhp_valid['S'][i] \n",
    "    \n",
    "    # prepare the image width for 2D keypoint normalization.\n",
    "    if '/TS5/' in tmp_dict['filename']:\n",
    "        tmp_dict['width'] = 1920\n",
    "        tmp_dict['height'] = 1080\n",
    "    elif '/TS6/' in tmp_dict['filename']:\n",
    "        tmp_dict['width'] = 1920\n",
    "        tmp_dict['height'] = 1080\n",
    "    else:\n",
    "        tmp_dict['width'] = 2048\n",
    "        tmp_dict['height'] = 2048\n",
    "    \n",
    "    mpi3d_val_list.append(tmp_dict)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def normalize_screen_coordinates(X,mask, w, h):\n",
    "    assert X.shape[-1] == 2\n",
    "\n",
    "    # Normalize so that [0, w] is mapped to [-1, 1], while preserving the aspect ratio\n",
    "    return (X / w * 2 - [1, h / w] ) * mask\n",
    "\n",
    "def get_2d_pose_reorderednormed(source):\n",
    "    reorder = [14,2,1,0,3,4,5,16,12,18,9,10,11,8,7,6]\n",
    "    tmp_array = source['kpts2d'][reorder][:, :2]\n",
    "    mask = source['kpts2d'][reorder][:, 2:]\n",
    "    tmp_array1 = normalize_screen_coordinates(tmp_array, mask, source['width'], source['height'])\n",
    "    return tmp_array1, mask\n",
    "\n",
    "def get_3d_pose_reordered(source):\n",
    "    reorder = [14,2,1,0,3,4,5,16,12,18,9,10,11,8,7,6]\n",
    "    tmp_array = source['kpts3d'][reorder][:, :3]\n",
    "    mask = source['kpts3d'][reorder][:, 3:]\n",
    "    return tmp_array, mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert the pose to 16 joints and put into array\n",
    "\n",
    "mpi3d_mask_check = []\n",
    "mpi3d_data_2dpose = []\n",
    "mpi3d_data_3dpose = []\n",
    "\n",
    "for source in mpi3d_val_list:\n",
    "    tmp2d, tmp_2dmask = get_2d_pose_reorderednormed(source)\n",
    "    tmp3d, tmp_3dmask = get_3d_pose_reordered(source)\n",
    "    assert np.sum(np.abs(tmp_2dmask - tmp_3dmask))== 0\n",
    "#     mask_check.append(tmp_2dmask)\n",
    "\n",
    "    if not np.sum(tmp_2dmask) == (len(tmp_2dmask)):\n",
    "        mpi3d_mask_check.append(tmp_2dmask)\n",
    "        \n",
    "    mpi3d_data_2dpose.append(tmp2d)\n",
    "    mpi3d_data_3dpose.append(tmp3d)\n",
    "\n",
    "mpi3d_data_2dpose = np.array(mpi3d_data_2dpose)  \n",
    "mpi3d_data_3dpose = np.array(mpi3d_data_3dpose)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mpi3d_mask_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2929, 16, 3)\n",
      "(2929, 16, 2)\n"
     ]
    }
   ],
   "source": [
    "# save the npz for test purpose\n",
    "\n",
    "print(mpi3d_data_3dpose.shape)\n",
    "print(mpi3d_data_2dpose.shape)\n",
    "np.savez('./test_set/test_3dhp.npz',pose3d=mpi3d_data_3dpose,pose2d=mpi3d_data_2dpose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load and check\n",
    "mpi3d_npz = np.load('./test_set/test_3dhp.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2929, 16, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate([mpi3d_npz['pose2d']]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show some pose from self defined idx\n",
    "idx = 1200 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mpi_inf_3dhp_test_set/TS3/imageSequence/img_000751.jpg'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpi3d_val_list[idx]['filename']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vals2d = mpi3d_data_2dpose[idx]\n",
    "ax2d = plt.axes()\n",
    "show2Dpose(vals2d, ax2d)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vals3d = mpi3d_data_3dpose[idx]\n",
    "fig3d = plt.figure()\n",
    "# ax = fig.add_subplot(1, 1, 1, projection='3d')\n",
    "ax3d = Axes3D(fig3d)\n",
    "show3Dpose(vals3d, ax3d)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
